Dear Sean,

CreditMetrics  package in R is a good resource. However, it would convenient
for me to comment further if you please be a little specific regarding  your
end purpose.

Kind Regards,
Debashis


On 05/11/2008, Sean O'Riordain <seanpor@acm.org> wrote:
>
> Good evening,
>
> I'm quite new to the risk modeling arena and recently I starting to
> look at some loans and their state transitions. For simplicity I will
> describe a cut-down version - NORM=normal, OD means between 1 and 89
> days after missing a payment and NPL means that there is a payment
> which is more than 89 days overdue.  My base data is the daily state
> transition list and the end of month state of the loan book.  I
> calculate the 3x3 probability transition matrix using a generator
> matrix method - loosely based on [1].
>
> For transition  / generator matrices, has anybody any suggestions for
> how I might look at the robustness / sensitivity of the calculations?
> My own thought was to calculate the matrix say N times leaving out a
> sample of 1/N of the input loans, and then look at the summary() type
> stats for each point in the matrix - thoughts? or is this just daft
> talk?  In a single variable I can plot the output density and look at
> the summary() data - but I have no clue really how to do this for a
> transition matrix.
>
> Then there is the question of time varying transition matrices and how
> to understand them and even <gasp> estimate where the matrix is headed
> over the next three months?
>
> Has anybody any suggestions as to where I might look for other ideas
> on where to go with this or has anybody done this type of loan book
> credit risk modeling?
>
> Many thanks in advance!
>
> Best regards,
> Sean O'Riordain
> Dublin, Ireland.
> seanpor at acm.org
>
>
> [1] Lando, D., and T. Skodeberg (2002). "Analyzing ratings transitions
> and rating drift with continuous observations." Journal of Banking and
> Finance 26: 423-444.
>
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